Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add zgbrenner/agentcounsel --skill pia-generationgit clone --depth 1 https://github.com/zgbrenner/agentcounselWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zgbrenner/agentcounsel/pia-generation)<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/pia-generation"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/pia-generation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/pia-generation"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/pia-generation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.03768 |
| Opus 5 | $0.00021 | $0.01884 |
| Sonnet 5 | $0.00008 | $0.00754 |
| Haiku 4.5 | $0.00004 | $0.00377 |
Grade A, and why
Privacy Impact Assessment scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Privacy Impact Assessment
Purpose
Produce a structured, attorney-ready draft Privacy Impact Assessment (PIA) for a proposed or recently changed processing activity. The PIA documents what personal data is involved, how it flows, where the activity may diverge from the organization's stated privacy commitments, what risks are specific to the design, and what mitigations with assigned owners would reduce those risks. The output is a draft for privacy-counsel review and sign-off — not an approval of the processing activity, not legal advice, and not a determination that any assessment is legally required.
Whether a formal Privacy Impact Assessment or Data Protection Impact Assessment (DPIA) is legally mandated for a given activity — and under which framework — is a legal determination that must be resolved by privacy counsel. This skill supports the internal scoping and drafting process; it does not resolve that determination.
Use When
- A product, engineering, or business team is launching a new feature, vendor integration, or processing activity that involves personal data, and an internal PIA is needed before sign-off.
- An existing processing activity is being materially changed — expanded data categories, new access paths, new subprocessors, changed retention, or changed purpose — and the PIA must be updated.
- Privacy counsel or a data-protection officer has asked for a PIA draft as an input to their review.
- A procurement or legal-ops team needs a structured first-pass assessment before routing to privacy counsel.
- Internal policy requires a PIA for new processing activities, and a structured draft is needed to initiate that workflow.
Required Inputs
- Description of the processing activity: what the feature, system, vendor use, or change does in functional terms. If not provided, stop and request it.
- Data categories: the specific fields or data elements involved — not generic labels like "user data" or "personal information." If only generic labels are provided, request specifics before proceeding.
- Data subjects: who the personal data relates to (e.g., customers, employees, job applicants, minors, website visitors). If unknown, flag as
[CONFIRM: data subjects]. - Purpose of processing: the stated business or functional purpose for which the data is collected or reused.
- New collection or reuse: whether this activity involves collecting data for the first time or reusing previously collected data for a new purpose.
- Access: who within the organization, and which systems, can access the data; whether access is role-restricted.
- Storage: where the data is stored (jurisdiction, cloud region, on-premises) and who controls the infrastructure.
- Retention period: how long the data is kept and what triggers deletion or archival. If unknown, flag as
[CONFIRM: retention period — [deadline verification required]]. - Vendors and subprocessors: any third parties that receive, process, or store the data, or that provide infrastructure used to process it.
- Failure modes: known or foreseeable failure scenarios — breach, unauthorized access, data loss, misuse, re-identification.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 156 lines · 42 tokens per session scan A bc7c06cea99f
Privacy Impact Assessment is a skill published in the GitHub repository zgbrenner/agentcounsel (19 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 3,768 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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